Conda? Mamba? Docker? Figuring Out Package Management Without Losing Your Mind
Ever tried installing a tool, only to break your entire setup in the process due to conflicting dependencies? Luckily, you don’t need to suffer. Let’s talk about how to manage your tools without losing your mind or your day.
Why Is Installing Bioinformatics Software So… Chaotic?
Because bioinformatics is built on an ever-shifting mountain of dependencies. Different tools require different versions of Python, R, C libraries, or compilers, and installing one thing can break another.
Enter package managers. Your personal tool-wrangling assistants.
Conda: The Handy Tool Depot
If you’re new to the bioinformatics world, Conda is your best friend.
Conda is both a package manager and an environment manager. It lets you create isolated environments where each tool and its dependencies live in harmony. No more having to worry about versions or dependencies (unless they are in the same environment).
Example:
conda create -n rnaseq salmon=1.10 fastqc=0.11.9
conda activate rnaseq
Now you’ve got a clean environment for your RNA-seq work.
Mamba: Conda, But Faster
Conda is great, but also kind of slow sometimes. Especially when it is trying to build large environments with many potentially conflicted dependencies.
Mamba is a drop-in replacement for Conda with the same commands, but lightning-fast installs thanks to C++ under the hood.
Install it once:
conda install mamba -n base -c conda-forge
Then use it like this:
mamba create -n variantcall bcftools=1.17 samtools=1.16
Trust me, you’ll feel the difference.
Channels Matter
When you install tools with Conda or Mamba, it pulls them from different channels (aka sources). Order matters!
The bioinformatics golden rule:
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
The --add parameter will add the specified channel to the top of the channels list, making it the highest priority. Therefore in the previous example, tools are downloaded from the channels in the following priority list: conda-forge, bioconda, then defaults.
Enter: Docker
Sometimes, even Conda isn’t enough. Maybe the tool is ancient. Maybe the developers didn’t build a Conda recipe. Maybe it only runs on a full moon. This is when you break out Docker.
Think of Docker as a portable, self-contained box that comes preloaded with everything your tool needs: OS, libraries, dependencies, and more.
You run the box. It works the same on every computer. Magic.
Example:
docker pull quay.io/biocontainers/fastqc
docker run -v $PWD:/data quay.io/biocontainers/fastqc fastqc /data/myfile.fastq
Not beginner-friendly, but super reliable once you’re comfortable.
Which Should You Use?
| Use Case | Best Tool |
|---|---|
| You’re just getting started | Conda |
| You’re sick of waiting on Conda | Mamba |
| You need max reproducibility | Docker |
| You’re using a workflow system | Conda or Docker |
| You want to share a toolset | Docker or environment.yml |
Quick Tips for Package Management Sanity
- Never install everything in your base environment
- Always use named environments (one per project is a good rule)
- Keep a copy of your environment:
conda env export > env.yml
- Re-create it anywhere:
conda env create -f env.yml
- If something breaks? Delete the env and start over. It’s fine.
Final Thoughts
You’re gonna install a lot of tools in your bioinformatics journey and, unfortunately, some of them will fight you. That’s normal.
But with Conda (and maybe Docker down the road), you can keep things clean, organized, and mostly stress-free.
Next up: we’ll chat about getting comfortable with the command line because learning a few terminal tricks can save you hours of frustration.
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